{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to load the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import swami    # SWAMI library\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mcm = swami.SwamiModel()  # defaults to MCM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Density is 2.603e-10 g/cm3\nTemperature is 175.26 K\n"
     ]
    }
   ],
   "source": [
    "out = mcm.run(\n",
    "    altitude=100,\n",
    "    latitude=3,\n",
    "    longitude=15,\n",
    "    local_time=12,\n",
    "    day_of_year=53,\n",
    "    f107=70,\n",
    "    f107m=69,\n",
    "    kp1=1,\n",
    "    kp2=1,\n",
    ")\n",
    "dens = out[\"MCM\"][\"dens\"]\n",
    "temp = out[\"MCM\"][\"temp\"]\n",
    "print(f\"Density is {dens:.3e} g/cm3\")\n",
    "print(f\"Temperature is {temp:.2f} K\")"
   ]
  },
  {
   "source": [
    "## Altitude profile"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "altitudes = np.arange(0.0, 300, 10)\n",
    "temp = []\n",
    "dens = []\n",
    "\n",
    "for h in altitudes:\n",
    "    out = mcm.run(\n",
    "        altitude=h,\n",
    "        latitude=3,\n",
    "        longitude=15,\n",
    "        local_time=12,\n",
    "        day_of_year=53,\n",
    "        f107=70,\n",
    "        f107m=69,\n",
    "        kp1=1,\n",
    "        kp2=1,\n",
    "    )\n",
    "    dens.append(out[\"MCM\"][\"dens\"])\n",
    "    temp.append(out[\"MCM\"][\"temp\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "f, ax = plt.subplots(1, 2, sharey=True)\n",
    "\n",
    "ax[0].plot(dens, altitudes)\n",
    "ax[0].set_xscale(\"log\")\n",
    "ax[0].set_xlabel(\"Density / g/cm³\")\n",
    "ax[0].set_ylabel(\"Altitude / km\")\n",
    "\n",
    "ax[1].plot(temp, altitudes)\n",
    "ax[1].set_xlabel(\"Temperature / K\")\n",
    "\n",
    "ax[0].grid(True)\n",
    "ax[1].grid(True)"
   ]
  },
  {
   "source": [
    "## Map at altitude"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "lati = np.arange(-90, 90, 10)\n",
    "loct = np.arange(0, 24, 3)\n",
    "\n",
    "temp = np.zeros((len(lati), len(loct)))\n",
    "dens = np.zeros((len(lati), len(loct)))\n",
    "\n",
    "for i, lat in enumerate(lati):\n",
    "    for j, lt in enumerate(loct):\n",
    "        out = mcm.run(\n",
    "            altitude=160,\n",
    "            latitude=lat,\n",
    "            longitude=15,\n",
    "            local_time=lt,\n",
    "            day_of_year=53,\n",
    "            f107=70,\n",
    "            f107m=69,\n",
    "            kp1=1,\n",
    "            kp2=1,\n",
    "        )\n",
    "        dens[i,j] = out[\"MCM\"][\"dens\"]\n",
    "        temp[i,j] = out[\"MCM\"][\"temp\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Local time / h')"
      ]
     },
     "metadata": {},
     "execution_count": 7
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1080x360 with 4 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "f, ax = plt.subplots(1, 2, figsize=(15, 5))\n",
    "\n",
    "lt, la = np.meshgrid(loct, lati)\n",
    "\n",
    "c = ax[0].contourf(lt, la, dens)\n",
    "f.colorbar(c, ax=ax[0])\n",
    "ax[0].set_title(\"Density / g/cm3\")\n",
    "ax[0].set_ylabel(\"Latitude / deg\")\n",
    "ax[0].set_xlabel(\"Local time / h\")\n",
    "\n",
    "c = ax[1].contourf(lt, la, temp)\n",
    "f.colorbar(c, ax=ax[1])\n",
    "ax[1].set_title(\"Temperature / K\")\n",
    "ax[1].set_ylabel(\"Latitude / deg\")\n",
    "ax[1].set_xlabel(\"Local time / h\")\n",
    "\n",
    "    \n"
   ]
  },
  {
   "source": [
    "## DTM"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Density is 1.827e-13 g/cm3, with uncertainty 7.4 %\nTemperature is 726.31 K\n"
     ]
    }
   ],
   "source": [
    "dtm = swami.SwamiModel(\"DTM2020\")\n",
    "\n",
    "out = dtm.run(\n",
    "    altitude=200,\n",
    "    latitude=3,\n",
    "    longitude=15,\n",
    "    local_time=12,\n",
    "    day_of_year=53,\n",
    "    f107=70,\n",
    "    f107m=69,\n",
    "    kp1=1,\n",
    "    kp2=1,\n",
    "    get_uncertainty=True\n",
    ")\n",
    "dens = out[\"DTM2020\"][\"dens\"]\n",
    "dens_unc = out[\"DTM2020\"][\"dens_unc\"]\n",
    "temp = out[\"DTM2020\"][\"temp\"]\n",
    "print(f\"Density is {dens:.3e} g/cm3, with uncertainty {dens_unc:.2} %\")\n",
    "print(f\"Temperature is {temp:.2f} K\")"
   ]
  },
  {
   "source": [
    "## UM"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Density is 2.603e-10 g/cm3, with standard deviation 2.474e-11\nTemperature is 175.26 K, with standard deviation 13.59\n"
     ]
    }
   ],
   "source": [
    "um = swami.SwamiModel(\"UM\")\n",
    "\n",
    "out = um.run(\n",
    "    altitude=100,\n",
    "    latitude=3,\n",
    "    longitude=15,\n",
    "    local_time=12,\n",
    "    day_of_year=53,\n",
    "    f107=70,\n",
    "    f107m=69,\n",
    "    kp1=1,\n",
    "    kp2=1,\n",
    "    get_uncertainty=True\n",
    ")\n",
    "\n",
    "dens = out[\"UM\"][\"dens\"]\n",
    "dens_std = out[\"UM\"][\"dens_std\"]\n",
    "temp = out[\"UM\"][\"temp\"]\n",
    "temp_std = out[\"UM\"][\"temp_std\"]\n",
    "\n",
    "print(f\"Density is {dens:.3e} g/cm3, with standard deviation {dens_std:.3e}\")\n",
    "print(f\"Temperature is {temp:.2f} K, with standard deviation {temp_std:.2f}\")"
   ]
  }
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